A simple and natural interpretations of the DAMPE cosmic-ray electron/positron spectrum within two sigma deviations

The DArk Matter Particle Explorer (DAMPE) experiment has recently announced the first results for the measurement of total electron plus positron fluxes between 25 GeV and 4.6 TeV. A spectral break at about 0.9 TeV and a tentative peak excess around 1.4 TeV have been found. However, it is very difficult to reproduce both the peak signal and the smooth background including spectral break simultaneously. We point out that the numbers of events in the two energy ranges (bins) close to the 1.4 TeV excess have $1\sigma$ deficits. With the basic physics principles such as simplicity and naturalness, we consider the $-2\sigma$, $+2\sigma$, and $-1\sigma$ deviations due to statistical fluctuations for the 1229.3~GeV bin, 1411.4~GeV bin, and 1620.5~GeV bin. Interestingly, we show that all the DAMPE data can be explained consistently via both the continuous distributed pulsar and dark matter interpretations, which have $\chi^{2} \simeq 17.2$ and $\chi^{2} \simeq 13.9$ (for all the 38 points in DAMPE electron/positron spectrum with 3 of them revised), respectively. These results are different from the previous analyses by neglecting the 1.4 TeV excess. At the same time, we do a similar global fitting on the newly released CALET lepton data, which could also be interpreted by such configurations. Moreover, we present a $U(1)_D$ dark matter model with Breit-Wigner mechanism, which can provide the proper dark matter annihilation cross section and escape the CMB constraint. Furthermore, we suggest a few ways to test our proposal.Comment: 18 pages, 6 figures, 5 tables. Figures and Bibs update

Decadal variation of prediction skill for Indian Ocean dipole over the past century

Indian Ocean dipole (IOD) is one of the dominant modes of interannual variability in the Indian Ocean, which has global climate impacts and thus is one of the key targets of seasonal predictions. In this study, based on a century-long seasonal hindcast experiment from the Coupled Seasonal Forecasts of the 20th century (CSF-20C), we show that the prediction skill for IOD exhibits remarkable decadal variations, with low skill in the early-to-mid 20th century but high skill in the second half of the 20th century. The decadal variations of prediction skills for IOD are caused by two factors. The first is associated with the decadal variation of the ENSO-IOD relationship. Although individual members of the predictions can simulate the variation of the ENSO-IOD relationship, with amplitude close to that in the observation, the feature is greatly suppressed in the ensemble mean due to the asynchrony of variation phases among individual members. In the ensemble mean, the IOD evolution shows an unrealistic stable and high correlation with ENSO evolution. This causes the prediction to have much higher skill for those periods during which IOD is accompanied by ENSO in the observation. The second factor is associated with the decadal variation of IOD predictability in the prediction system. In the prediction system, the decadal variation of IOD signal strength closely follows that of ENSO signal strength. Meanwhile, the IOD noise strength shows variations opposite to the IOD signal strength. As a result, the signal-to-noise ratio greatly increases in the second half of the 20th century due to the enhancement of the ENSO signal strength, which represents the increase of IOD predictability in the prediction system

Amplification of synoptic to annual variability of West African summer monsoon rainfall under global warming

Increased knowledge of future changes in rainfall variability is needed to reduce vulnerability to potential impacts of global warming, especially in highly vulnerable regions like West Africa. While changes in mean and extreme rainfall have been studied extensively, rainfall variability has received less attention, despite its importance. In this study, future changes in West African summer monsoon (WASM) rainfall variability were investigated using data from two regional climate models that participated in the Coordinated Regional Climate Downscaling Experiment (CORDEX). The daily rainfall data were band-pass filtered to isolate variability at a wide range of timescales. Under global warming, WASM rainfall variability is projected to increase by about 10–28% over the entire region and is remarkably robust over a wide range of timescales. We found that changes in mean rainfall significantly explain the majority of intermodel spread in projected WASM rainfall variability. The role of increased atmospheric moisture is examined by estimating the change due to an idealized local thermodynamic enhancement. Analysis reveals that increased atmospheric moisture with respect to warming following the Clausius–Clapeyron relationship can explain the majority of the projected changes in rainfall variability at all timescales.publishedVersio

Present situation and development prospects of the diagnosis and treatment of rotator cuff tears

Rotator cuff tears are an important cause of shoulder pain and are caused by degeneration or trauma of the shoulder tendon at the anatomical neck of the humeral head. The understanding and research of rotator cuff tears have a history of hundreds of years, and their etiology, diagnosis, and treatment have a complete system, but some detailed rules of diagnosis and treatment still have room for development. This research paper briefly introduces the diagnosis and treatment of rotator cuff tears. The current situation and its valuable research direction are described

The effect of horizontal resolution on the representation of the global monsoon annual cycle in Atmospheric General Circulation Models

The sensitivity of the representation of the global monsoon annual cycle to horizontal resolution is compared in three Atmospheric General Circulation Models (AGCMs): the Met Office Unified Model-Global Atmosphere 3.0 (MetUM-GA3), the Meteorological Research Institute AGCM3 (MRI-AGCM3) and Global High Resolution AGCM from the Geophysical Fluid Dynamics Laboratory (GFDL-HiRAM). For each model, we use two horizontal resolution configurations for the period 1998–2008. Increasing resolution consistently improves simulated precipitation and low-level circulation of the annual mean and the first two annual cycle modes, as measured by pattern correlation coefficient and Equitable Threat Score. Improvements in simulating the summer monsoon onset and withdrawal are region-dependent. No consistent response to resolution is found in simulating summer monsoon retreat. Regionally, increased resolution reduces the positive bias in simulated annual mean precipitation, the two annual-cycle modes over the West African monsoon and Northwestern Pacific monsoon. An overestimation of the solstitial mode and an underestimation of the equinoctial asymmetric mode of the East Asian monsoon are reduced in all high-resolution configurations. Systematic errors exist in lower-resolution models for simulating the onset and withdrawal of the summer monsoon. Higher resolution models consistently improve the early summer monsoon onset over East Asia and West Africa, but substantial differences exist in the responses over Indian monsoon region, where biases differ across the three low-resolution AGCMs. This study demonstrates the importance of a multi-model comparison when examining the added value of resolution and the importance of model physical parameterizations for the Indian monsoon simulation

TEMPERA: Test-Time Prompting via Reinforcement Learning

Careful prompt design is critical to the use of large language models in zero-shot or few-shot learning. As a consequence, there is a growing interest in automated methods to design optimal prompts. In this work, we propose Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge, is adaptive to different queries and provides an interpretable prompt for every query. To achieve this, we design a novel action space that allows flexible editing of the initial prompts covering a wide set of commonly-used components like instructions, few-shot exemplars, and verbalizers. The proposed method achieves significant gains compared with recent SoTA approaches like prompt tuning, AutoPrompt, and RLPrompt, across a variety of tasks including sentiment analysis, topic classification, natural language inference, and reading comprehension. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods